Method and apparatus for enhancing an image using data optimization and segmentation

ABSTRACT

A method and system are disclosed for generating enhanced images of multiple dimensional data using a depth-buffer segmentation process. The method and system operate in a computer system modify the image by generating a reduced-dimensionality image data set from a multidimensional image by formulating a set of projection paths through image points selected from the multidimensional image, selecting an image point along each projection path, analyzing each image point to determine spatial similarities with at least one other point adjacent to the selected image point in a given dimension, and grouping the image point with the adjacent point or spatial similarities between the points is found thereby defining the data set.

This application claims the benefit of Provisional application Ser. No.60/130,226, filed Apr. 20, 1999.

The U.S. Government has license rights in this invention and the rightin limited circumstances to require the patent owner to license otherson reasonable terms, as provided for by the terms of Contract No.RO1EB002639, awarded by the National Institutes of Health.

BACKGROUND OF THE INVENTION

The present invention relates generally to data image processing and,more specifically, to generating enhanced digital images utilizing imagesegmentation processes that involve depth buffer information inassociation with point selection projection images.

The development of high resolution 3D (three-dimensional) imagingtechniques, such as magnetic resonance imaging (MRI), computedtomography (CT), and rotation X-ray CT angiography (XRCTA), have led tosituations where high volumes of image data are generated in the studyof individual patients. In many cases, such as in 3D magnetic resonanceangiography (MRA) and 3D CT angiography (CTA), the amount of detail pertwo-dimensional slice is small, but the high resolution detail of thefull image volume is required in the diagnostic process. In one example,arterial beds are generally sparse, so there is typically a large amountof nonvascular image data present in a 3D angiographic image. As theresolution and volume coverage of MRA examinations have increasedrecently, having adequate means to effectively and efficiently reviewthe available image information is a significant challenge forradiologists. Currently, there are three primary display techniques usedfor clinical evaluation of MRA data. The first technique is to displayselected original cross-sectional MRA images. The second technique is todisplay oblique planar reformatted images. The last technique is todisplay a maximum intensity projection (MIP) image. The followingdiscussion focuses mostly on MRA images; however, the discussion isequally applicable to other 3D angiography techniques such as CTA andXRCTA and to other volumetric visualization techniques in whichselectable groups of image points are of interest.

The original cross-sectional images, also referred to as slices, containthe maximum amount of information on a local level. Each image displaysthe transverse segments and cross sections of vessels that intersect theplane of the image. Sometimes, the vascular detail of interest may beobserved in a single image. More often, because of the intricate pathsof the vessels, many images must be viewed, and the information fromeach image must be integrated to formulate an understanding of thestructure of interest. This method is inefficient since it requires asingle slice to be displayed, which slice contains very little globalinformation about any single intricate vessel in the overall vascularnetwork.

The oblique planar reformatted images provide improved efficiency overthe original images by displaying image planes that follow a segment ofa vessel of interest through the three-dimensional image data. Eachindividual image still provides only local information and a completeview of the vascular bed is not obtained. Volume rendering consists ofthe projection or rendering of an entire 3D image volume onto a singletwo-dimensional image. The volume is projected along parallel ordiverging lines through the three-dimensional volume onto atwo-dimensional image. The intensities along the projection line aretransformed according to some specified transformation. Such rendering,in a variety of forms, has become very useful in assisting the observerin the rapid and efficient interpretation of the large amounts of imagedata originally obtained. A simple form of volume rendering, which isalso intuitive, is an X-ray-like summation of the image densities orintensities. Initial attempts at MRA found that when the summation typeof volume rendering was used, the background signal was too large to thepoint of masking a majority of vascular details during the summationprocess. The display capabilities were improved to the point whereuseful vascular details were observable when the rendering was performedby selecting only the maximum image value encountered along eachprojection line. This approach is known as the maximum intensityprojection (MIP) algorithm and has been used in the development of avariety of MRA techniques. Other forms of volume rendering, such asassigning opacities and translucensies to certain image values orregions of image values, have been applied and found useful. However,the MIP algorithm is dominant because of its simplicity and consistencyin generating quality images.

The MIP algorithm is successful to the extent that the signal fromvessels is greater than the signal of the surrounding tissues. Inregions where vessels do not appear overlapped, the MIP algorithm issufficient to display vessels that are hyperintense relative to thevariations in the overall background. Unfortunately, the MIP algorithmdoes not provide any information about vessels that are hidden below theintensity of other structures. Because of the sparseness of vessels,there is a significant amount of vessel detail that is not hidden andthe MIP performs very well in increasing the amount of vessel detailobserved in a single image.

Although there is a loss of information in a MIP image, the MIPalgorithm provides a large amount of useful information in a singledisplay. The information density, or information content per imageelement, is much higher in a MIP image than in the original 3D sourcedata. In other words, although the source image data contains more totalinformation, including some small arteries that would not appear in theMIP image, the density of vessel information in the source image data(i.e. the percentage of image elements associated with vessels) is lowerthan in the MIP image. As discussed later, many investigators have triedto develop algorithms to overcome the limitations of the MIP algorithm,and, although these have been viewed as improvements, the improvementshave not been sufficient for any of these algorithms to replace the MIPalgorithm. The advantages of the MIP algorithm typically outweigh thedisadvantages found therein. These advantages include-reduced dynamicrange, generally consistent image display, and improved signaldifference to noise ratio (SDNR or contrast to noise ratio) for vesselsthat appear in the MIP. The artifacts in the MIP, although very real,are also well understood and can be “read through.”

The MIP display contains a large amount of global information about thevascular system. The appearance of a MIP image is quite similar to thatof an X-ray angiogram, but there are several differences. The MIP simplyselects the image element with the maximum intensity along eachprojection line, while the X-ray projection is a summation of alldensitometric information along each projection line. The MIP image istherefore a flat display having no depth information and no informationabout the thickness of the vessel through which the projection linepassed, while in X-ray angiography the vessel brightness or darknessdepends directly on the length of the X-ray projection path through thevessel. Thus, in regions where several vessels are overlapping in theMIP image, it is difficult to resolve specific vessel segments. Further,because the signal intensity is not related to the projection pathlength through the vessel, an increase in vessel signal is not observedfor foreshortened vessels. There are other limitations that exist in theMIP algorithm. Statistical nosie properties associated with the signalof the tissue background cause the signal level of the background in theMIP to increase with the projection volume thickness. Consequently,small vessels with signal levels that are slightly above the localbackground signal level may be lower than other background imageelements along the projection line and, therefore, may not be observedin the MIP images. The limitations of the MIP can make it necessary toalso review the original low information density cross-sectional images.When used in combination, the two display formats are a means fordetecting and evaluating many types of vascular pathology. The reviewprocess, however, is rather time consuming, especially for complexcases.

Various attempts have been made to overcome the deficiencies that existin the MIP algorithm. The appearance of small vessels in the MIP imagecan be improved by the use of zero filled interpolation (ZFI). Theproblem addressed by ZFI is that the arbitrary positions of the vesselsrelative to the reconstruction grid cause small vessels or vesselborders to be poorly rendered in the image due to the weighting of thevoxel sensitivity function. ZFI reconstructs the image on a grid that isfiner than the acquisition grid, thereby reducing vessel jaggedness inthe MIP image caused by partial volume effects and improving vesselcontinuity and visibility in the MIP image for subvoxel-sized vessels.

Other attempts to overcome the problems inherent in the MIP algorithmand to improve the two-dimensional rendering of MRA image data rangefrom simple modifications of the MIP algorithm, to attempts at completesegmentation and redisplay of the vascular information contained in theoriginal MRA data. These attempts include utilizing the traveling orsliding slab MIP technique; preprocessing the original MRA image databefore application of the MIP algorithm; line-based segmentation, wherethe vessel voxels are segmented from the background image data, which isalso similar to segmentation work performed in other fields; intensitybased segmentation, where the vessel voxels are segmented based uponrelative intensities and proximity in 3D; and interactive intensitybased segmentation, where vessel voxels that cannot be segmented basedupon intensity are displayed over a wider gray scale range than thosethat can be segmented.

MRA display capabilities are also useful in a number of intracranialapplications. There are various disease processes for which thediagnosis can benefit from improved image display capabilities. Thesediagnostic capabilities include predicting the need for thrombolysis instroke conditions, diagnosing vasculitis and other occlusive diseases,identifying intracranial tumors and arterial venous malformations, andperforming preoperative assessments. Further, MRA and other 3Dangiographic images provide useful assistance for surgical procedures.The display of catheter angiograms and/or MRA image data have been foundto be important aids during aneurysm surgery. The usefulness of thedisplay of 3D angiographic image data during surgery can be enhanced bythe display of the angiographic images in conjunction with 3D images ofother types of anatomy such as adjacent bone structures or criticalorgans.

SUMMARY OF THE INVENTION

According to the present invention, a method and system are disclosedfor enhancing and segmenting a multi-dimensional image based upon thedepth buffer (or “Z-buffer”) that is generated in conjunction with amaximum intensity projection (or related point selection projection)operation through the multi-dimensional image. This enhancement andsegmentation is referred to as the depth-buffer segmentation (DBS)process. The DBS process segments an MA image volume into regions havinga likelihood of vessel occupation and regions that are unlikely tocontain vessels. The process reduces the total image volume to a muchsmaller volume so that the amount of territory that a user must cover isgreatly reduced. In addition, projection images of the vascularinformation found within the data become much more refined and visiblewhen the background regions have been removed.

The DBS process merges the global properties of the MIP process with thelocal properties of continuity to achieve a high specificity in thesegmentation of vessel voxels from background voxels while maintaining ahigh sensitivity. The segmentation provides an accurate and robust viewof the vessel structure and filters out most of the non-vessel regionsin the image. After the vascular detail is segmented, it can then beconverted back to image data for display—for example, as a densitometricsummation of the MRA image data resembling that of an X-ray angiogram.In an optional step, the dynamic range of the display is reduced byhollowing out the vessel structures and displaying the vascular detailsas X-ray projection through hollow tubes. Further, the image may bemodified by adding a degree of shading so that the 3D vessel structureis apparent in a manner not visible in X-ray images. Such image shadingwas not possible in the MIP process.

In one embodiment, the method generates a reduced dimensionality imagedata set from a multi-dimensional image by formulating a set ofprojection paths through image points selected from themulti-dimensional image, selecting an image point along each projectionpath, analyzing each image point to determine spacial similarities withat least one other point adjacent to the selected image point in a givendimension, and grouping the image point with the adjacent point orspacial similarities between the points is found thereby defining thedata set. The method, within the analyzing step, the step of determiningsimilarity of brightness between the image point and the adjacent point.Further, the analyzing step also determines similarity of smoothnessbetween the image point and the adjacent point. In one example, thesmoothness is determined by using a least squares fit of adjacent imagepoints. In yet an alternative embodiment, the method further includesselecting another image point along the projection path and performingthe analyzing and grouping steps on the newly selected image point.Further still, the method may also convert the grouped and ungroupedimage points into a multi-dimensional image and then perform regiongrowing within the converted multi-dimensional image or performhollowing-out of the multidimensional image for image enhancement.Another image enhancement steps include removing all pixels that aresurrounded on each side by an adjacent pixel prior to displaying theimage of the merged and unmerged image points.

The system further defines an image processing apparatus that comprisesmeans for defining a set of projection paths through a multi-dimensionalimage, means for selecting at least one point, along each projectionpath, based upon a specified criterion, means for formulating an arrayof projection values corresponding to the positions of selected pointsalong their respective projection path, and means for grouping aselected number of projection values based upon their proximity to otherprojection values. The apparatus essentially is a programmable computersystem that loads a particular software program capable of implementingthe steps of the method claims within a computer architectureenvironment for manipulating the data and processing it for imaging,whether the imaging is in a printed image or a displayed image, such ason a computer monitor. Further still, the method may be implemented in acomputer program product for sale or distribution, whether that productis in a portable medium or in a fixed medium or remote medium that iscommunicated via a communications source, such as the Internet or othercommunication means known to those skilled in the art.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will become more fully apparent from the followingdescription and appended claims, taken in conjunction with theaccompanying drawings. Understanding that these drawings depict onlytypical embodiments of the invention and are, therefore, not to beconsidered limiting of its scope, the invention will be described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 illustrates a block diagram of the imaging system utilizingdepth-buffer (or Z-buffer) segmentation in accordance with the presentinvention;

FIG. 2 illustrates a flow diagram of the method steps implemented by theimaging apparatus of FIG. 1 in rendering DBS images in accordance withthe present invention;

FIG. 3 illustrates a standard axial MIP image of a patient with a largeophthalmic artery aneurysm;

FIG. 4 illustrates a depth-buffer or Z-buffer array corresponding toFIG. 3 in which brightness is proportional to the “Z” location of eachpoint selected for the MIP along each projection line;

FIG. 5 depicts a plot of the intensity values of the data from theprojection lines for the 30 points indicated by the short line thatcrosses the middle cerebral artery as shown in FIG. 4. The z-locationsof the maximum intensity values projected by the MIP algorithm areindicated by the vertical marks.

FIGS. 6 through 8 illustrate an expanded view of the small region of theZ-buffer array of FIG. 4 where FIG. 6 illustrates example line segmentsused for the least squares fit roughness calculation, FIG. 7 illustratesimage brightness inversely proportional to the roughness values inaccordance with the present invention, and FIG. 8 illustrates the sameregion with brightness set proportional to group size after grouping lowroughness pixels that have similar depth values—i.e. have connectivityin the Z-buffer,

FIGS. 9 through 11 illustrate a series of images where each imagerepresents where FIG. 9 is a brightness inversely proportional to theroughness value in accordance with the present invention, FIG. 10 is abrightness based upon number of points connected in the Z-buffer, whereconnectivity is based upon local smoothness and proximity in the depthor “Z” direction, and FIG. 11 is a brightness proportional to number ofpoints connected after connectivity has been enhanced by the 3D regiongrowing process;

FIGS. 12 through 14 illustrate part of an original 3D image with thecorresponding segmented image and also the segmented image overlayed onthe original image.

FIG. 15 illustrates the performance of the segmentation based on avessel and non-vessel classification of the segmented image of FIGS. 12through 14 as performed by an independent observer;

FIGS. 16 through 18 illustrate stereo pair X-ray-like densitometricprojections (DPs) of the DBS segmentation corresponding to the image ofFIG. 3, where FIG. 16 is the DP of all segmented vessels, FIG. 17 is theanterior circulation, and FIG. 18 is a combination of DP and shadedsurface display of a hollow representation of the DBS segmentation ofthe anterior and posterior circulations;

FIGS. 19 through 22 illustrate stereo pair X-ray-like DPs, based on theprinciples of the present invention, of the intracranial circulation ofa patient with a posterior communicating artery aneurysm and a basilartip aneurysm;

FIGS. 23 through 24 illustrate stereo pair X-ray-like DPs of a contrastenhanced abdominal aorta in accordance with the present invention; and,

FIGS. 25 through 26 illustrate a set of CTA images including (A) axialcollapse of segmented data structures and (B) shaded hollow-bodyreprojection in accordance with the present invention.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

It will be readily understood that the components of the presentinvention, as generally described and illustrated in the figures herein,could be arranged and designed in a wide variety of differentconfigurations. Thus, the following more detailed description of theembodiments of the system and method of the present invention, asrepresented in FIGS. 1 through 26, is not intended to limit the scope ofthe invention, as claimed, but is merely representative of the presentlypreferred embodiments of the invention. Likewise, the followingdescription focuses on the capability of the present invention toisolate blood vessels for enhanced angiographic imaging, but this is notintended to limit the potential uses for which the invention may be ofvalue to only those examples given.

The presently described embodiments of the invention will be bestunderstood by reference to the drawings, wherein like parts aredesignated by like numerals throughout.

FIG. 1 illustrates a block diagram of an imaging apparatus 10 inaccordance with the present invention. Imaging apparatus 10 comprises animaging device 12, a Z-buffer MIP processor 14, an image processor 16, astorage system 18, output device 20, and image viewing device 22. Eachelement is connected with at least one other element within apparatus 10or, alternatively, each element is connected to a common bus 23 thatallows communication to and from each element with any other elementwithin apparatus 10.

Imaging device 12 typically is a magnetic resonance imaging (MRI) systemor a computed tomography (CT) system or a rotational X-ray CTangiography (XRCTA) system used in generating 3D images in anon-invasive manner of a subject, such as an object or a patient, whichimages can be stored in computer manipulable form for subsequentprocessing and display. Z-Buffer MIP processor 14 and storage system 18are typically selected as a personal computer-type system or workstation. One such system includes a personal computer based on theAdvanced Micro Devices (AMD) Athlon 650 megahertz CPU with a storagesystem comprising 128 megabytes of RAM and a 20 gigabyte long-termstorage hard drive. Alternatively, the computer system can be selectedfrom other known computer systems as previously mentioned, such as forexample, a work station like that provided by Sun Computers, such astheir UltraSpark Platform. The computer system further incorporates theimage processor 16. One image processor suitable for use is provided byMitsubishi and is known as the VP 5003D graphic accelerator. Thepersonal computer system operates in a Windows NT operating systemenvironment. Other operating systems include Windows 98 and Windows2000, which are both provided by Microsoft of Redmond, Wash. Further,the invention can be implemented on an Apple compatible system runningunder OS9 and configured in a comparable manner to the personal computermentioned above.

Output device 20 can include such items as a printer system or removablestorage device. The printing systems typically utilized as outputdevices offer a high quality output such as those found in digitalphotography printers. Other printers, including low resolution laserprinters, would also be acceptable in some situations. Typically,however, the use of better resolution in printing is desirable. Imageviewing device 22 typically is a video monitor. The monitor typically isa high resolution monitor given the large amount of image detail that isof interest. The depth-buffer segmentation process makes it feasible toinclude a graphical user interface to allow a user to select differentviewing options, different angles of view, rotational viewing options,and other animation-type viewing options, which enhance and optimize theviewing needs of the user.

The operation of apparatus 10, as well as the process for converting rawimage data information into a useful depth-buffer segmentation (DBS)image, is shown in the flow diagram of FIG. 2 and is presented ingreater detail below in accordance with the present invention. In orderto begin processing an image, an image must be generated or taken of thesubject matter to be examined. The image or images can come from variousimaging modalities. These modalities can be selected from magneticresonance imaging systems or other 3D imaging systems. Other imagingmodalities include computed tomography (CT), computed tomographyangiography (CTA), and X-ray CT angiography (XRCTA) as well as othertypes of 3D imaging systems known to those skilled in the art. Further,it is the image data itself that is useful and processed as will beshown below.

In experiments made to evaluate the process of the present invention,image data acquisition was performed as part of an aneurysm imagingprotocol. The imaging was performed on a 1.5 Tesla (T) General Electricmodel Signa MRI scanner with actively shielded gradients. The gradientsystem operates with a maximum strength of 23 milli-Tesla/meter (mT/m)and a slew rate of 77 milli-Tesla/meter/millisecond (mT/m/ms) on allthree axes for the MRA sequence. An optimized cylindrical birdcagetransmit/receive coil with an RF mirror endcap is used. Generally, theimage is acquired in any desired technique, one of which is illustratedin the acquisition of step 212, which uses a three-dimensionaltime-of-flight (TOF) sequence. The 3D TOF sequence incorporates imagingtechniques such as abbreviated magnetization transfer, flow compensationin the frequency and slice-encode directions, and RF spoiling, which areunderstood by those skilled in the art. For this image acquisitionprocess, the imaging parameters used and that are understood by thoseskilled in the art were: repetition time (T_(R))=48 msec, echotime(T_(E))=5.3 msec, readout bandwidth (RBW)=±16 kHz, “in-plane”matrix=512×192 image elements, field of view (FOV)=220×165 mm².

The next step, step 214, although optional for the present invention,applies zero filled interpolation in all 3 image directions. After theoptional interpolation, step 216 applies MIP processing to the 3D imagedata MIP processing involves generating a 2D image from the 3D image,where the image value selected for each point in the 2D image is themaximum (or minimum) image value found along a corresponding linethrough the 3D image. The MIP projection lines may all be parallel, asin a parallel ray MIP image, or the lines may be non-parallel and forexample might diverge from a single source point as in a divergent rayMIP image or in curvilinear space as well.

In step 218 an array is generated from the original 3D image data bydetermining the depth or Z-positions of the points included in the MIPimage. In this Z-buffer array, the value of each element is related tothe distance of the corresponding point in the MWP image from the 2Dimage plane (i.e. the location of the maximum (or minimum) intensityvalue along each projection line). FIG. 3 illustrates a standard axialMIP image of a patient having an ophthalmic artery aneurysm. The imagewas acquired in a 512×256×64 image format and then interpolated to1024×1024×128, which is shown in step 214. The image is generallyconsidered to have high quality, but the artifacts inherent in the MIPalgorithm, such as vessels obscured by other vessels, are visible. FIG.4 displays the depth-buffer (or Z-buffer) array corresponding to theaxial MIP image of FIG. 3. As noted, the Z-buffer array values,displayed here as levels of brightness, are proportional to the depth or“Z” positions of the points that appear in the MIP image of FIG. 3.

The generation of the MIP with the Z-buffer may actually be formed inany direction through the original 3D MRA image data A convenientdirection that is chosen in the samples previously mentioned and that isused in the current embodiment is to perform the MIP in the originalslice selection direction. Typically, the path will be along a definedline taken for evaluation, whether it be a straight line, a divergentline, or a curvilinear line, as determined by the operator at the timethe evaluation is performed.

FIG. 5 illustrates a plot of signal intensities along the projectionlines in the original 3D image data for 30 points taken from FIG. 3. Thevertical marks on the 30 plots of FIG. 5 show the positions and valuesof the points that projected into the MIP image in FIG. 3. Each of theselines corresponds to a point in the vertical bar that crosses the rightmiddle cerebral artery shown in the display of the Z-buffer array ofFIG. 4.

Two observations are made looking at these illustrations. The first isthat the vessels exhibit very high continuity in position in the “Z”location. This continuity is used in a primitive vessel detectionalgorithm that generates a value based on the smoothness in at least onedirection around a point. For example, the intensities in the image ofFIG. 7 are inversely related to the minimum χ² value obtained from thefirst order fit of 5 points in the four principal directions around eachpoint of FIG. 6, which is a magnified view of the region indicated witha small rectangle in FIG. 4. The four principal directions are definedas 0, 45, 90 and 135 degrees from horizontal in the 2D image plane. Forthose points where the χ² value is low in at least one direction (smoothneighborhood), the image is bright. As will be shown, the vesselsegments visible in the MIP of FIG. 3 are all characterized bysmoothness and continuity. In view of this, the process of performingdepth-buffer segmentation consists in grouping image points based onthis smoothness and continuity. The major vessels are characterized bylarge groups of points.

The second observation is that the apparent width of the vessel“continuity” region in the Z-buffer array display is noticeably widerthan the apparent width of the vessels in the MIP image (FIG. 4 ascompared to FIG. 3). This difference in width is explained by notingthat the slow flow of blood near the vessel edges results in very lowsignals. Yet, the signals at the vessel edges can still be sufficientlystrong to cause many of these edge points to project in the MIP image.Hence, the edge points are present in FIG. 3 even though theirbrightness is scarcely distinguishable from the background. In theZ-buffer array of FIG. 4, the edge points are seen to have the samedepth as the brighter central points of FIG. 3, confirming that theypertain to the same anatomical structure.

The apparatus and process of the present invention measures the localroughness for each element within the Z-buffer array in accordance withstep 220. The process measures the roughness (or smoothness), asdescribed below, in the Z-buffer array. In most areas of the Z-bufferarray, only pixel groups from vessels exhibit smoothness in one or moredirections. To measure the smoothness, the process performs a low orderpolynomial least squares fit to Z-buffer array values (MIP imageZ-locations) in the four principal directions around each point in theZ-buffer array. The four directions are shown in the upper left handquadrant 600 of FIG. 6. Beginning with the brightest point in the MIPimage, every point in the MIP image is tested for classification asvessel based upon local smoothness in the corresponding Z-buffer array.If the element is found to be locally “smooth” the process tests alleight neighbors in two dimensions for additional possible vesselelements. If the neighboring elements are also “smooth” and if they areclose in “Z,” they are added to the group and their neighbors are addedto the list for testing. The process continues until all neighbors tothe group are too “rough” or too separated in “Z” to be connected.

The process then considers the next nonclassified, brightest point inthe MIP image and tests whether it is a “smooth” element. If thiselement is smooth, it is defined as the next group and the methodrepeats the process of looking for additional members to add to this newgroup. The process continues until all elements in the Z-buffer arrayhave been tested.

FIG. 6 illustrates an expanded view of a small region of the Z-bufferarray of FIG. 4. The four small line segments, segments 610-616, showthe length (5 pixels) of the segment used for the first order leastsquares fit. Points 610 and 614 are outside vessels and yield a high χ²value for the fit in any direction. Point 612 is within a vessel, butyields a high value except for the direction parallel to the vessel.Point 616 is within the vessel and yields a low value for three out offour possible directions. FIG. 7 illustrates the same region as FIG. 6,where the image brightness of each pixel is inversely proportional toits minimum calculated roughness value, or χ² value, as specified below.The χ² value for the fit in the jth direction (j=1,2,3,4) for the ithelement in the Z-buffer array is:$\chi_{ij}^{2} = {\sum\limits_{k}\quad \left( {Z_{ijk} - P_{ijk}} \right)^{2}}$

where Z_(ijk) is the Z-buffer value (depth-buffer value) for elementnumber k (k=1,2,3,4,5) along the line having element i at its center anddirection j. The P_(ijk) corresponds to a predicted value for the sameelement determined from a low order polynomial fit to the values alongthe line. The brightness in the array shown in FIG. 7 is then inverselyrelated to the minimum χ² value. For each point i and all correspondinglines j=1,2,3,4:

χ_(i) ²=MIN_(j)(χ_(ij) ²)

and brightness, V_(i), is

V _(i) =A/(B+χ _(i) ²)

where the constants A and B are empirically determined. The brightnessvalues displayed in FIG. 7 tend to increase with the likelihood that theelement is part of a vessel.

Other methods of measuring smoothness in the MIP Z-buffer could be used.The current method involves performing a low order least squares fit toa small number of image elements centered at a particular point. In thecurrent embodiment, the process utilizes a first order fit along fivepoints, and an example of applying the fit in four principal directionsis shown in FIG. 6. Alternatively, a different number of points, adifferent order fit, and a different number of directions may be used inmeasuring local roughness or smoothness as desired by the systemdesigner. Or the process could be omitted completely in someimplementations.

As may be deduced from FIG. 6, only points that are contained withinvessels will have a very low χ² value. Points just outside the vesseledges generally experience erratic Z-buffer depth values at some pointalong each of the lines selected for the fit, resulting in high χ²values. Because a vessel only requires smoothness in one direction, theprocess saves only the minimum value of the χ² for each point. Theminimum χ² image is illustrated in. FIG. 9 illustrates an image wherethe brightness is inversely proportional to the minimum χ² value of theleast squares fits of short line segments along the principaldirections.

It is then possible to look throughout the minimum χ² image for regionsof connected “smoothness” as performed in Step 222 of FIG. 2 in order todetermine regions that are most likely to be vessels. Such regions areindicated by the grouped image elements of FIGS. 12 through 14. FIGS. 12through 14 illustrates points in the Z-buffer that are grouped togetherbased upon their low roughness values and proximity. The brightnessshown in FIGS. 12 through 14 is proportional to the number of points ineach group.

In the implementation of step 222, the process performs a groupingoperation where each data point is considered in a specific order. Theuse of the MIP image implies that the bright parts of the original threedimensional image data were the most important. As such, the processperforms the connectivity operations by selecting the brightest imageelement in the MIP image. This element is tested for a low value in thecorresponding minimum χ² array. If the minimum χ² is below a predefinedthreshold, the point is selected as a possible vessel and theneighboring points in the 2D MIP image are tested. The brightest elementand all of its neighbors and their neighbors, and so forth, whichsatisfy the connectivity criterion, are added to the group. The processthen considers the next non-classified brightest point and determineswhich remaining neighboring points satisfy the connectivity criterion toform another group. The process continues until all points have beentested. To be connected in this embodiment of the invention, the minimumχ² value must be below a given threshold and the Z-position must bewithin a small distance of the currently considered point. For example,in this illustration, the threshold value for χ² equaled 1.0 and a stepof +/−2 in Z was allowed. Other values may be selected by the designeraccording to the needs of the designer, but this step size recognizesthat larger vessels occasionally project from different regions withinthe vessel, and the larger value improves the character of theconnectedness. FIGS. 16 through 18 illustrates an image of the pointsthat have been grouped based upon the proximity in “Z” and minimumroughness. The intensity of the display is proportional to the number ofpoints in each group. It is shown that some vessels, although obviously“connected” in real life, are not connected by this implementation ofthe process (e.g. the middle cerebral artery is not connected to thetrifurcation area). These disconnects happen because only one point in athick vessel is projected by the MIP algorithm, and the thickness of thevessel allows larger jumps in “Z” than are accepted by the connectivitycriteria used in this example.

Once all the contiguous groups of minimum roughness are determined inthe MIP Z-buffer array, there are still many vessels that are crossed byother vessels and remain broken. Further, only one point per projectionline is included in the vessel group. It is therefore useful to map eachgroup of vessel points back to their original three dimensional imagepositions where a region growing algorithm is utilized for each point ineach group to add additional points from the three dimensional imagedata to each vessel group as shown in step 224. In step 224, to completethe process of connecting all vessels, the groups are mapped back to thethree dimensional image space, and all points from the two dimensionalgroups are considered again in the same order. All neighboring points inthe 3D data are tested based upon their intensity being a selected (e.g.two standard deviations) above the average background value. Points thatare two standard deviations or more above the average background wouldhave projected in the MIP image had they not been obscured by otherbrighter structures, such as vessels. By adding these points to thegroups, the process fills out the vessels in 3D and also connects mostbranches that were “disconnected” by being obscured by other vessels. Animage of points connected by the process in step 224 is illustrated in11.

As indicated by step 226, extraneous groups are removed from the set ofgrouped image points created in step 224. For example, noise groups canbe removed automatically by eliminating all groups with very smallnumbers of elements. In this example, all groups with less than 45elements were considered to be noise and were removed. All the majorvessels subtend more than 45 elements, and very few noise groups havemore than 45 elements (voxels). Where groups with large numbers ofvoxels are not of interest, such as regions of fat or bone or organtissue in angiographic imaging, they can be eliminated by a manual step,that allows the user to select the groups to be removed and cause thosegroups not to display.

A qualitative evaluation of the results of the segmentation process canbe performed by comparing the elements segmented as belonging to vesselswith those seen in the original cross-sectional images. The DBS processperforms well in classifying vessel and non-vessel voxels. Themisclassifications consist of the smallest vessels that did not appearin the MIP and the centers of the carotid arteries that were dark in theoriginal 3D data. FIGS. 12 through 14 show an example of points in theoriginal 3D image that, as shown in FIG. 15, were manually classified asvessel and non-vessel by an independent observer. The points segmentedby the DBS process of the present invention as vessels and non-vesselsare shown as white and black regions of points in FIGS. 12 through 14.The manually classified points appear as white points in these samefigures. FIG. 15 illustrates graphs of the performance of thesegmentation based on the vessel and non-vessel classification of thesegmented image of FIGS. 12 through 14 as performed by an independentobserver. The graphs represents the vessel inclusion sensitivity asmeasured by the number of voxels in a group of a few hundred and of×1000. Line 910 represents very small vessels means only see in localprojections. Line 912 represents small vessels as seen in the MIPimages. Line 914 represents the medium size secondary bifurcations, M2,A2. Line 916 represents large vessels, such as internal carotid andmiddle cerebral arteries.

Once all of the data elements have been classified by the DBS algorithmas groups of structures such as vessels, any of the previously mentionedvariety of display techniques may be utilized, including MIPs and shadedsurface renderings. By displaying the original MRA intensities of onlythose points classified as vessel, it is straightforward to performdensitometric summations along lines to the original data. The resultingimages, as shown in FIGS. 16 through 18, look much like subtractionX-ray angiograms, but the dynamic range is so large that it is hard toportray the vessels at a single window/level setting on a video display.More specifically, FIG. 10 illustrates a stereo pair X-ray-likedensitometric projection through DBS process images from the aneurysmstudy of FIG. 3. FIG. 16 is a stereo pair of the fill intracranialcirculation while FIG. 17 illustrates a stereo pair of vessels connectedto the anterior and middle cerebral arteries. It is difficult to showthe full dynamic range of intensities contained in the images of FIGS.16 and 17. The dynamic range is reduced by removing all points internalto the DBS segmented structures. X-ray-like densitometric projectionusing the hollow DBS process in accordance with the present invention isshown in FIG. 18. In FIG. 18 some surface shading is also added to theX-ray-like projection image. In FIG. 18, the intracranial carotid arteryelement 1010 is visible below the aneurysm. Thus, the processmanipulates the dynamic range of information for display as shown instep 228 of FIG. 2 to enhance the resolution of the vessel structures ofinterest during the display.

As just noted, one example of performing dynamic range reduction is toeliminate all data points within the image that have neighbors in everydirection. This results in an image where the vessels appear as hollowtubes. Typical displays of densitometric projections through the hollowvessel images are shown in FIG. 18 and in FIGS. 19 through 26. Thecharacterization of the image may be further modified by adding anamount of shading to each vessel surface thereby enabling the observerto determine whether the vessels are near or far, and the vesselorientation is more discernable as well. Once the final manipulation ofthe dynamic range is performed, the process, as shown in step 230,displays the processed image on an imaging device, such as a videomonitor and/or prints the image on a printing apparatus. Optionaldisplay processes are contemplated that may yield more visualinformation. The present embodiment of the invention may not eliminateall noise, but the noise is reduced to the point that the useful data iseasily recognized over these noise points.

FIGS. 19 through 22 illustrate stereo pair X-ray-like densitometricreprojections of the results of the DBS segmentation of points from the3D image of a patient with a communicating artery (PCOM) aneurysm and abasilar tip aneurysm. FIG. 19 is the cranial view of the PCOM aneurysmwhile FIG. 20 is the caudal view. FIG. 21 highlights the posteriorcommunicating artery aneurysm 1108 while FIG. 22 illustrates thehighlight of a small basilar tip aneurysm, which is behind tip 1110 andis shown from two different orientation.

FIGS. 23 through 24 illustrates a stereoscopic X-ray-like densitometricreprojection through a renal artery study in accordance with the presentinvention. FIG. 23A shows the anterior view while FIG. 24 illustratesthe posterior view.

Lastly, FIGS. 25 through 26 represents an example of CTA images acquiredat relatively low resolution on a helical CT scanner. Segmentation isperformed with the DBS process in accordance with the present invention.FIG. 25 depicts the axial collapse of the segmented data structures,while FIG. 26 illustrates shaded hollow-body reprojections of the aorticaneurysm.

The DBS process as described and presented in the present inventionresults in an image segmentation process that is readily applicable tomagnetic resonance angiography (MRA), computed tomography angiography(CTA), rotational X-ray angiography (XRCTA), and other medical andnon-medical applications. Since the DBS process is based upon thegeneric MIP algorithm, the application of the DBS process can beextended wherever the MIP algorithm is currently being used. The utilityof the DBS process can be enhanced to include display options that allowthe user to toggle between the DBS process and the preliminary MIPprocess, as well as other forms of volume rendering. The DBS process isalso applicable to such fields as computer assisted screening and imageinterpretation based upon segmented anatomy.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims, rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed is:
 1. A method of grouping image data points based onsmoothness or roughness values, the method comprising: creating areduced-dimensionality image data set from a multidimensional image dataset; selecting a first set of points in the reduced-dimensionality imagedata set, each point in the first set of points having a correspondingpoint in a second set of points in the multidimensional image data set;defining a unique projection path for each point in the first set ofpoints, the projection path extending in a direction Z from a point inthe first set of points through the corresponding point in the secondset of points; determining a distance measure for a first point in thefirst set of points, the distance measure being the distance along theprojection path in the Z direction between the first point in the firstset of points and the first point's corresponding point in the secondset of points; determining the distance measures for multiple points,including an image point, in the first set of points; calculating asmoothness or roughness value for the image point in the first set ofpoints by comparing the distance measure of the image point to thedistance measures of other points in the first set of points, each ofsaid other points in the first set of points being in proximity to theimage point; and grouping the image point with similar points in thefirst set of points, each of said similar points having, both (a)proximity to the image point, and (b) a smoothness or roughness valuerelated to the smoothness or roughness value of the image point.
 2. Themethod according to claim 1, wherein the smoothness or roughness valueof the image point is determined using a least squares fit of thedistance measures of points in proximity to the image point.
 3. Themethod according to claim 1, wherein a Z-buffer array comprises thedistance measures of multiple points in the first set of points.
 4. Themethod according to claim 3, further comprising applying a process tothe Z buffer array to enhance the Z buffer array based upon expectedproperties of adjacent points in the Z buffer array.
 5. The methodaccording to claim 4, wherein the process comprises measuring arrayelement roughness in a plurality of directions around each array elementin the Z buffer array.
 6. The method according to claim 1, furthercomprising converting grouped and ungrouped points into amulti-dimensional image.
 7. The method according to claim 6, furthercomprising performing region growing within the multi-dimensional image.8. The method according to claim 7, further comprising hollowing out themulti-dimensional image.
 9. The method according to claim 8, wherein thehollowing out comprises removing each pixel from a group that issurrounded on each side by a pixel from said group.
 10. The methodaccording to claim 1, further comprising displaying an image of groupedand ungrouped image points.
 11. The method according to claim 1, whereinthe multidimensional image data set is a magnetic resonance derivedimage set.
 12. The method according to claim 1, wherein themultidimensional image data set is a computed tomography derived imageset.
 13. The method according to claim 1, further comprisingcompensating for variations in sensitivity along projection paths toenhance a projection image.
 14. The method according to claim 1, whereinthe projection paths are curvilinear.
 15. The method according to claim1, wherein the projection paths are divergent from a point of origin.16. The method according to claim 1, wherein the proximity is defined asbeing no more than two image element positions from the image point. 17.The method according to claim 1, wherein the proximity is based on pointadjacency.
 18. The method according to claim 1, further comprisingmanipulating image groups for enhanced display.
 19. The method accordingto claim 18, wherein the image manipulation consists of hollowing imagestructures.
 20. The method according to claim 19, wherein the hollowingstep comprises removing voxels in a group that are surrounded inmultiple directions by adjacent voxels in the same group.
 21. The methodaccording to claim 1, further comprising displaying a resulting image.22. The method according to claim 21, wherein the display comprisessummation of the multi-dimensional image along projection lines.
 23. Themethod according to claim 21, wherein the display comprises shadingvolume surfaces.
 24. The method according to claim 1, further comprisingidentifying bifurcations or branches of groups segmented from themultidimensional image.
 25. The method of claim 1, wherein acorresponding point in the second set of points comprises a maximumintensity value along the corresponding point's projection path.
 26. Themethod of claim 1, wherein a corresponding point in the second set ofpoints comprises a minimum intensity value along the correspondingpoint's projection path.
 27. The method of claim 1, wherein acorresponding point in the second set of points comprises a value aboveor below a predefined value.
 28. The method of claim 27, wherein thecorresponding point in the second set of points comprises an intensityvalue above an average background value.
 29. The method of claim 28,wherein the corresponding point in the second set of points comprises anintensity value more than two standard deviations above the averagebackground value.
 30. The method according to claim 1, wherein thesmoothness or roughness value of the image point is determined usingchi-square values of the fit of the distance measures of points inproximity to the image point.